A Flask-based e-commerce targeting system for customer segmentation and personalized product recommendations. Upload structured interaction data, receive AI-driven suggestions, and analyze user behavior.

Ensure you have the following installed:

• Python 3.8+
• pip (Python package manager)
• Git (for cloning the repository)

On Windows:

python -m venv venv
venv\Scripts\activate

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

On Windows:

set FLASK_APP=run.py
set FLASK_ENV=development

On macOS/Linux:

export FLASK_APP=run.py
export FLASK_ENV=development

flask run

🔗 Open http://127.0.0.1:5000 in your browser.

• Upload your dataset.
• Match required and additional columns.
• Run the segmentation or recommendation.
• View results.

• Open config.py.
• Set DEBUG_MODE = True.

deactivate

visitorid,itemid,event
12345,6789,view
54321,1234,transaction

visitorid,total_views,total_addtocart,total_purchases
12345,10,2,1
54321,5,1,0

Listed in requirements.txt:

flask
pandas
numpy
scikit-learn
matplotlib
seaborn
scipy

pip list | grep -E 'flask|pandas|numpy|scikit-learn|matplotlib|seaborn|scipy'

• Dependencies Not Installing: Ensure correct versions of Python and pip.
• Server Not Starting: Verify FLASK_APP is set to run.py.
• File Upload Issues: Ensure the dataset is in CSV format.

# Check Python and pip versions
python --version
pip --version
pip install --upgrade pip

# Check Flask app variable
echo $FLASK_APP

# Check file type
file uploads/sample.csv

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m "Added new feature")
4. Push to the branch (git push origin feature-branch)
5. Create a pull request

This project is licensed under the MIT License.

🌟 If you found this project helpful, please ⭐ the repository!